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Wang, Joanne Pei-Chung and Gutierrez, Anabel (2019) An In-Store Mobile App for CustomerEngagement: Discovering Hedonic and Utilitarian Motivations in UK Grocery Retail. In: Themistocleous,Marinos and da Cunha, Paulo Rupino, eds. Lecture Notes in Business Information Processing.Information Systems: 15th European, Mediterranean, and Middle Eastern Conference, EMCIS

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https://doi.org/10.1007/978-3-030-11395-7_20

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https://kar.kent.ac.uk/72964/

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Author's Accepted Manuscript

15th European Mediterranean & Middle Eastern Conference on Information Systems

EMCIS 2018, Limassol, Cyprus 4-5 October 2018.

An In-Store Mobile App for Customer Engagement:

Discovering Hedonic and Utilitarian Motivations

in UK Grocery Retail

Joanne Pei-Chung Wang1 and Anabel Gutierrez2

1 Regent’s University London, Digital Marketing & Analytics, London, UK

[email protected] 2 University of Kent, Digital Marketing & Analytics, Kent, UK

[email protected]

Abstract. This paper investigates the hedonic and utilitarian motivations that may influence UK grocery consumers to adopt and use new features proposed for an in-store mobile app. The scope of this research is to develop a conceptual model that reflects the motivations for using an in-store mobile app to engage customers. Two pilots were conducted to explore possible attributes for hedonic and utilitarian motivations found in literature, and factor analysis was used to test their validity. A survey with the final items selected was used to collect data from a large UK grocery retailer resulting in a sample of 633 customers. The results supported that utilitarian motivations for grocery shopping include time convenience, performance expectancy and information availability. For the hedonic motivations, the attributes supported include idea motivation, personalisation, value motivation and experiential shopping. Although previous research conceptualised user control as an important utilitarian motivator, this research found that this attribute correlates similarly to both, hedonic and utilitarian motivations. Possible implications are that regardless of customers’ hedonic or utilitarian preferences, it is always essential for customers to have the ability to choose and customise what data and communications they share and receive for successful in-store mobile app engagement.

Keywords: Mobile App · Technology Adoption · Utilitarian Motivation · Hedonic Motivation

1 Introduction According to the IGD (Institute of Grocery Distribution), the UK Grocery sector was valued at £179 billion in 2016, of which non-grocery items made up £13 billion and grocery comprised £166 billion [53]. After several years of slumping sales, it has been reported that purchasing levels have been relatively flat, increasing pressure for grocers to compete for market share [39].

There has been substantial growth in online grocery purchasing and in the UK this channel is expected to grow 68% by 2021. The UK is also leading in online grocery compared to the rest of Europe with 6.9% of UK FMCG (Fast Moving Consumer Goods) sales done online [46]. In April 2016, 48% of Brits were purchasing groceries online via supermarket website and mobile apps for delivery or store collection. Millennials (born between the early 1980s to early 2000s) seem to have a higher propensity for online shopping than other customer segments [11]. Amongst 25-34-year-olds, 23% did all their grocery shopping online, followed closely by 20% of 35-44-year-olds. In contrast, two age segments did not follow this trend. Only 9% of 45-54-year-olds and 5% of shoppers age 55+ did all their grocery shopping online [11].

mailto:[email protected]

mailto:[email protected]



However, while Mintel forecasts online grocery retailing to reach £16.7 billion in

2021, and UK grocers have invested heavily in developing apps and websites to support delivery and ‘click & collect’ services, online grocery shopping currently only accounts for an estimated 5% of total grocery sales [10]. About one-quarter of all UK grocery shoppers have purchased groceries online, three-quarters have not, and 24% of Brits had never bought groceries online and had no interest in doing so, rising to 38% of Brits aged 55+ [14]. There are several reasons that customers prefer shopping in-store instead of online, such as the lack of control when choosing fresh products, high delivery charges, limitations in product range or because they find that prices are lower, which might also indicate that they are shopping more at discounters [11].

Nonetheless, it cannot be ignored that smartphones have become a pervasive and integral part of people’s lives with an estimated 43.1 million mobile users in the UK [21]. Along these lines, if the majority of customers still prefer to purchase groceries in-store, then there are untapped opportunities to create a better shopping experience by adapting grocery mobile apps to serve UK customer where over 90% of retail happens. 1.1 Grocery Market Digital Ecosystem Some retailers, particularly in the US, have designed apps with ‘Store Mode’, enabling customers to use their mobile in-store to view dynamic store maps, find exact product locations and follow the most efficient routes through a store to fulfil a shopping list. Adding Store Mode features to a retailer's app has been shown to drive five times more shopper engagement while increasing sales and customer loyalty. Research showed not only five times more interactions with the Store Mode app, but also that the average number of shopping list items increased 1.5 times. The fastest growing segment of shoppers is those using store mode 5 or more times in a month [45].

Walmart introduced ‘store mode’ in 2012 with functionalities such as a voice-activated shopping list, a ‘Scan & Go’ feature that provides extensive product information, the ability to access purchase history and conduct e-commerce on the go [35]. If an item on a customer’s shopping list is not available in-store, it will let them instantly order it via the app for delivery. By using geofencing technology, the store mode automatically switches on when entering the store and offers localised ads for that particular store, so customers know what is on sale on that day [33].

At the same time, brick-and-mortar supermarkets such as German discounter Aldi are expanding their digital presence, while pure-play online retailers such as Amazon are opening physical stores. Aldi’s aggressive brick & mortar expansion across the UK continues apace, but Aldi also launched its first online presence in the UK with a £35 million investment [8], selling wine and non-food items for now. AmazonFresh partnered with Morrisons to offer 1-hour delivery to selected postcodes in London in July 2016 [9]. Additionally, Amazon is in the process of acquiring organic foods supermarket Whole Foods, which has nine stores in the UK [39]. Amazon has also made global headlines with its ‘just walk out technology’ AmazonGo store in Seattle, WA, the beta test was open to employees only and opened to the public in January 2018 becoming the first brick-and-mortar convenience store with no checkout lines and no cashiers [18, 19]. 1.2 Loyalty Customers Drivers Loyalty programmes are a marketing strategy to retain and derive more revenue from customers in the future [37]. Many loyalty programs have shown success by increasing attitudinal as well as behavioural loyalty [15]. However, loyalty is driven by likeability and trust rather than reward schemes and points [11]. This means that for brands the equilibrium between implicit (quality and trust) and explicit (rewards and points) loyalty drivers need to be achieved in order to ‘incentivise the right behaviour and



entice people to come back’.

Kantar WorldPanel conducted an interesting study on the drivers of customer loyalty considering the top UK grocery retail brands as perceived by shoppers across five different categories [43]: an enjoyable place to shop cares about me offers good value for money convenient inertia & proximity

Customer engagement has received considerable attention from researchers due to the strength businesses receive when creating a relationship with the customer [51]. Some arguments give particular importance to the fact that the concept of customer engagement extends to the definition of involvement, attachment and commitment [4]. To date, there is no agreed upon definition of customer engagement. However, Scholer and Higgins [40] define engagement as an active relationship with a brand that is established by the intensity of the customer’s psychological state. The authors characterise this psychological state by the emotional connection, sustained attention, brand relevancy and commitment to a brand. Fully engaged customers account for 23% more revenue than average customers, tend to buy more, promote the brand more to others and demonstrate more loyalty towards the company [12]. However, customer engagement is not a formula that can be applied to all companies, because each customer is different. Brands need to develop strategies to interact with their customers, building relationships with them through personalised messages and discounts, or even inspiring their loyalty and affection [41].

Marketers face a tremendous challenge in raising the public estimation of marketing. Across the UK, 42 percent of adults distrust brands and as high as 69 percent distrust online advertising [17]. Furthermore, University of Cambridge Psychometrics Centre reported in Marketing Week [11] that 71% of consumers worldwide feel that marketers use personal marketing data unethically, and 58% have been wary of engaging digitally via apps, email and social media due to concerns about misuse of personal data. Yet, the same study reported that 94% of marketers believe in the importance of using personal data for predictive analytics to engage consumers. This disparity in consumers’ versus marketers’ perspectives could be problematic and better understanding of customer engagement drives is essential.

This research aims to investigate the motivators that influence grocery customer’s intention to use an in-store mobile app and to propose a conceptual model that would provide insight into customer intentions to adopt in-store mobile apps. The next section will explain the theory used to explore the different customer's motivations and the initial motivations considered for the conceptual model. Next, the research design and method are presented. The proposed conceptual model is validated in section four followed by the conclusions. 2 Research Model Grocery shopping is perceived as a high-frequency and functionalistic activity [31], and this type of habitual and routine shopping activity is often considered by customers to be a chore [13]. While many factors are known to influence consumers shopping behaviours, utilitarian and hedonic motivators are considered by many researchers to be robust constructs when trying to understand customers’ behavioural intentions [2, 3, 44].

Utilitarian shopping motivation reflects the consumer’s desire for efficiency, rational and task-oriented efforts [1]. Consumers tend to use technological services, like



mobile apps, to simplify a process, in the form of performance expectancy, information availability and time convenience [45]. Additionally, user control can positively affect the outcome and the value of the channel as consumers perceive satisfaction when having more control of the process [27].

In contrast to utilitarian values, hedonic motivators are those factors that tend to trigger consumers’ emotions and feelings. While mobile apps are developed with a functional purpose, hedonic values may represent a meaningful way to stimulate the adoption intention of individuals to use mobile apps.

Given the multiple ways grocery retailers are incorporating technologies to their digital ecosystems, this research aims to investigate and test what the utilitarian and hedonic motivations that influence grocery customer’s intention to use an in-store mobile app are. 2.1 Utilitarian Motivations Time Convenience. Convenience or time savings is defined as the efficiency of shopping found through saving time [1]. Convenience is found to be a strong motivator for consumers to shop online and make use of mobile applications [44] while engaging them in various channels of shopping [1]. Smartphones, and specifically mobile applications, have become personal shopping assistants for customers, primarily due to the convenience aspect of the technology [42]. Performance Expectancy. Performance expectancy is a utilitarian value defined by the degree an individual believes that the usage of a technology simplifies the process [46]. In the context of mobile apps, the usage of this technology will enable users to accomplish their goal-oriented task [28, 46]. Research has shown that performance expectancy is a strong predictor of the intention of use, meaning that when performance expectancy increases individuals are more likely to continue to use the mobile app [30]. Information Availability. Information availability is defined as the availability to acquire information about the product, stores, promotions and other aspects [49]. Mobile applications provide efficient means for consumers to get information with few clicks. For example, mobile apps have the capability of delivering product information on a customer’s demand including a product’s location, nutritional value, price, offers, reviews, etc. [28]. User Control. The last value of utilitarian motivations considered for this research is user control, which is defined as the extent to which users can determine the content and sequence of the transaction [27]. Research has shown that consumers perceive technologies with higher values if they are able to have more control of the process and the technology [22] which is extremely important in order to comply with the new UK General Data Protection Regulations [24]. 2.2 Hedonic Motivations

Idea Motivation. Hedonic ideas motivation, refers to collecting information about new trends and products, more specifically in the context of shopping it refers to ‘keep up with trends’ [2, 26]. Customers enjoy browsing to obtain information about new trends and products, while not making a particular purchase [5]. Also, studies have found that consumers who seek product information more often tend to acquire more personalised and special products, due to their continuous research for latest trends [7]. Mobile applications allow customers to easily access information and promotions about products and services, which has shown to increase the idea shopping motivation [49]. This provides pleasure and positive experience as ‘a motive for the ongoing search’ [2], which may lead to a final purchase.



Personalisation. Personalisation is conceptualised in this research in terms of media richness, the extent to which a channel provides personalised content and emotional messages [16, 29]. Due to information technology and data-mining, many retailers have adopted practices to send personalised messages to individual customers as it can be a cost-effective and practical market tool [36]. Relevant information to individual consumers has shown to improve the effectiveness of mobile commerce strategies [20, 28]. Value Motivation. Value motivation focuses on bargain hunting and discount seeking behaviour of customers that explained the excitement of users when looking for discounts and the enjoyment of finding value in a purchase [34, 50]. Research has shown that many consumers that find discounts feel satisfaction and accomplishment [47]. Mobile applications have enabled consumers to find inexpensive shopping opportunities and coupons on-the-go while allowing them to share their findings instantaneously with other individuals [3, 50] proved that value motivation increases involvement and excitement in the shopping experience and now mobile devices have enabled individuals to fulfil this need with a few clicks of a button from anywhere [37]. Experiential Shopping. Experiential Shopping also known as ‘Adventure Motivation’ is a hedonic value that refers to the desire an individual has for an enjoyable, exciting and entertaining shopping experience [3, 48] It has been found that in-store and mobile app usage do not provide the same stimuli, but combining both experiences may have a positive impact on consumers’ behaviour [38]. Although different stimulus can be obtained throughout different channels, the usage of a mobile application for in-store shopping can stimulate sensory attributes and create enjoyment through the use of new technologies [22, 38]. 2.3 Behavioural Intention

Behavioural intention is a well established construct defined by [22] as the likeliness of an individual to perform a particular behaviour. Research has found that behavioural intention has been investigated in most studies of m-commerce and mobile applications [52], meaning that the construct is one of the most important to analyse in studies involving mobile technologies and its acceptance when exploring innovative features that may or not attract customers.

This research aims to investigate and validate which utilitarian and hedonic motivations may influence grocery customer’s intention to use an in-store mobile app. Figure 1, illustrates the conceptualisation for utilitarian and hedonic motives to be validated in this research.

Fig. 1. Conceptual Model

3 Research Design and Method The collection of data was conducted in a structured manner using a web-questionnaire. This type of survey allows respondents to choose their answers from predetermined options for reliability and validity [32]. Surveys are typically used to test variables simultaneously and the relationship of examined factors [23]. Pilot surveys were created and sent out using Google Forms to a convenience sample in order to test the



survey constructs and ensure question clarity (details will be covered later in this report). The actual final customer survey was set up using a different platform. While the pilot survey was sent primarily through social media channels, the final customer survey was sent via email to a customer panel for a large UK grocery retailer. 3.1 Survey Design

Two pilot tests were conducted to validate and reduce the 19 items developed from previous research to measure the conceptual model constructs using a 7 Likert scale. Pre-testing the survey is an important step to generate valuable data [32, 6, 23]. The variations between the surveys are explained below. Pilot 1 - Features Focused on using Compatibility, Hedonic & Behavioural Intention

Constructs The first pilot test was fielded on 7 Aug 2017, including 19 items sent to a convenience sample. The purpose of the pilot was to identify problems respondents might have with specific questions, improve the quality of the survey for clarity, and narrow down the items that will be more relevant for real customers. The first pilot was sent to a broad age group and various nationalities with a majority based in the UK and US. Respondents were asked to complete the survey and optional open-ended questions were available for feedback or comments in case any difficulties were encountered [32]. An exploratory factor analysis was conducted using SAS Enterprise Guide 7.1 to test the variability and correlation of the constructs. Although the outcome of the first pilot did not support the validity and reliability of the proposed constructs, it revealed valuable insights to improve the questionnaire. The results and the comments provided by the pilot participants informed the re-design of the survey as follows. Firstly, some items were removed, reworded and/or re-designed to ensure they closely reflected the construct they were measuring. Secondly, a case scenario format (Appendix A) was created describing the use of the proposed in-store mobile app features to ensure participants had the same scenario in mind when evaluating the hedonic and utilitarian dimensions. Final survey constructs, item attributes and statements are defined in a Table 1 below and use for the second Pilot. Pilot 2 - Scenario-based Survey Using Utilitarian, Hedonic & Behavioural Intention

Constructs The second pilot test was fielded on 15 Aug 2017, including ten items sent to a convenience sample. The exploratory factor analysis for this new data showed that all the hedonic questions and most of the utilitarian measures grouped together as anticipated. However, as two of the utilitarian items (UM4 and UM3) were loading high in both factors, both items were reworded to better match utilitarian definitions as conceptualised for the final survey.

Table 1. Final Survey Statements for Each Item Attribute



Construct Code Item attribute Survey statement

Utilitarian Motivations

UM1 Time Convenience

"Using this app while shopping in-store will add convenience and time savings"

UM2 Performance Expectancy

"Using this app will aid and simplify my in-store shopping process"

UM3 Information Availability

"Using this app while shopping in-store will make accessing relevant information available when and where I need it"

UM4 User Control "The ability to choose what notifications I want to receive, gives me control to use the app in the way that's most useful for me"

Hedonic Motivations

HM1 Idea Motivation "I would fancy using the app to discover 'what's new' and get meal ideas"

HM2 Personalisation "I like that the app delivers personalised content based on my preferences and favourites...and that I can adjust these preferences."

HM3 Value Motivation "I would enjoy using the app to find good values and discounts as I navigate through the store"

HM4 Experiential Shopping

"I would enjoy my shopping experience more by using the app"

Behavioral Intention

BI1 Behavioral Intention

"If the IN-STORE mobile app was developed to assist my grocery shopping, I would prefer this way of shopping over the way I shop today"

BI2 Behavioral Intention

"If these new app features were available, I would regularly use the supermarket's mobile app while shopping in-store"

3.2 Data Collection and Demographics

Respondents were able to access the survey online and answer the questions themselves [23]. Self-completion surveys are known to create less biased answers because there is no social desirability [6]. The final survey was designed with a scenario-based format. Scenarios are used to loosely sketch the user experience in the environment of use, enabling companies to gain valuable user input early in the development stage without having to commit significant resources [25]. Therefore, individuals can indicate their personal opinions without prior knowledge on the topic [23].

The survey presented instructions for participants and was kept as brief as possible to prevent respondent fatigue [6]. The survey was divided into four sections: Scenarios, Participants Intention, Utilitarian and Hedonic Motivations, and sent via email to 2,600 panel members. Demographic questions were not included as this information was captured and available in the panel’s member database.

Data quality is an essential measurement in research studies to determine the quality of the survey ‘as it influences the validity of the conducted analyses’ [23]. Typically, data quality is measured by variables like missing data and time to complete the questionnaire [23], but for the specific study, data quality will mainly focus on the



quality of responses.

The study had a 29.34% response rate with a total of 763 respondents, however, when looking at the dataset, it seemed like some respondents did not take the time to differentiate their answers, and therefore we assumed that they did not read the questions and just answered them to enter a prize drawing. Data was cleaned, respondents that answered the questionnaire with same numbers throughout all items were deleted from the dataset. Around 17% of respondents (130) had the same rating for all items. Thus, these were excluded from the study to achieve better data quality and the results. Conceptual validation was conducted with a sample of 633 valid responses.

Table 1. Sample Characteristics

Frequency Percent

Gender

Female 436.00 68.88

Male 197.00 31.12

Age Group

20-29 30.00 4.74

30-39 92.00 14.53

40-49 134.00 21.17

50-59 149.00 23.54

60-69 162.00 25.59

70+ 66.00 10.43

Table 1 shows that the sample includes 197 male respondents (31%) and 436

females (69%). Regarding age, most respondents were in between the ages of 60 and 69 years old (26%), followed by 50 to 59-year-old (24%). The lowest number of respondents were those aged 20 to 29, which correspond to about 5% of the whole sample. 4 Conceptual Model Validation and Preliminary Results The raw Cronbach coefficient alpha for hedonic motivations in the final sample is equal to 0.92 and Table 2 illustrates that all items correlation with the total score are acceptable and that none of the items deletion would improve the reliability. However, for the utilitarian motivations, one of the items (UM4) has lower correlation and its removal from the construct would increase the internal reliability to 0.93.

Table 2. Cronbach Coefficient Alpha with Deleted Variable

Construct subscale Items/Deleted Variable

Cronbach

Coefficient

Alpha

Raw Variables

Correlation Alpha

with Total

Hedonic Motivations 0.92

HM1 Idea Motivation

0.80 0.90

HM2 Personalisation

0.79 0.90

HM3 Value Motivation

0.84 0.88



HM4 Experiential Shopping

0.82 0.89

Utilitarian Motivations 0.90

UM1 Time Convenience

0.83 0.85

UM2 Performance Expectancy

0.84 0.85

UM3 Information Availability

0.85 0.85

UM4 User Control

0.61 0.93

The final validation for the conceptual model using a sample of 633 customers from a large UK grocery retailer is presented in Tables 3 and 4. Despite that user control (UM4) was hypothesised as a utilitarian motivation that increases the value that consumers perceive from technologies when they have more control [23], these research results showed that user control was similarly correlated to both, utilitarian and hedonic motivations. Furthermore, the Cronbach test confirmed that UM4, had low correlation compared to the other items and by removing UM4 the overall reliability could be improved. Validity and reliability for user control as an attribute for utilitarian motivations was not confirmed, we argue that user control might be a fundamental feature for any mobile applications rather than a utilitarian motivation and further research is needed to validate this assumption. Hence, UM4 was removed from the utilitarian construct and factor analysis was conducted to check the validity of the loadings for the final items as illustrated in Table 3.

Table 3. Rotated Factor Pattern Loading for the Final Conceptual Model

Rotated Factor Pattern

Items Factor1 Factor2

Time Convenience (UM1) 0.854 0.079

Performance Expectancy (UM2) 0.781 0.175

Information Availability (UM3) 0.612 0.324

Idea Motivation (HM1) 0.089 0.776 Personalisation (HM2) 0.158 0.712 Value Motivation (HM3) 0.251 0.692 Experiential Shopping (HM4) 0.291 0.631

Reliability of the three constructs presented in the proposed model is shown in

Table 4, together with the composite mean.

Table 4. Final Cronbach’s Alpha Score for Each Construct of the Conceptual Model

Construct Subscale Items

Cronbach's Alpha Score

Mean

Utilitarian Motivators UM1, UM2, UM3 0.93 4.66

Hedonic Motivators HM1, HM2, HM3, HM4 0.92 4.35

Behavioural Intention BI1, BI2 0.93 3.97

While customers rated higher the utilitarian motivations, their final intention to

adopt the new app was relatively lower than expected. Although further data analysis is needed to confirm the impact of hedonic and utilitarian motivations have on intention to adopt the new app, preliminary results showed that age has a significant effect on all constructs. For example, comparing participants who are considered Millennials (age 20-39) against those participant that belong to the Gen X or older (age 40-70+) a



significant difference is observed in the t-test illustrated in Figure 2.

Fig. 2. Intention to Adopt In-Store App by Generation

The proposed in-store mobile app features, provide an insight of the hedonic and

utilitarian attributes that could make the shopping experience easier and more enjoyable. Consequently, customers may feel more engaged and valued, increasing their loyalty. Further research is needed to provide grocers with greater customer insights and profitability. For example, the millennial generation is more innovative and early adopters of technology, hence the proposed app features may be adequate for targeting this audience. Although only 19% of the participants were Millennials, our results showed that participants who belong to this generation have significantly higher levels of intention to adopt the new app as illustrated in Fig. 2.

5 Conclusions This paper investigates the hedonic and utilitarian motivation that may influence UK grocery consumers to adopt and use new features proposed for an in-store app. Two pilots were conducted to discover valid and reliable attributes to conceptualise the hedonic and utilitarian futures for various new features in a proposed in-store mobile app. Having an engaging and indispensable in-store mobile app can be the key to success, and the validated proposed conceptual model would enable grocers to investigate its impact further.

The final validation of the conceptual model using a sample of 633 customers from a large UK grocery retailer provided a valid model that potentially would deliver insight into customer intentions to adopt an in-store mobile app. This research scope is limited to the validation of the conceptual model and further research will be conducted to identify the level of influence that hedonic and utilitarian motivators have in adopting the in-store mobile app proposed. Additionally, different demographics should be



tested to determine further relevant segments for which this motivator might vary. As an example, preliminary results indicated that Millennials might be the right target audience for the new app that could enable the grocer to collect data for using a combination of creative approaches and customer analytics.

A random sample from a large UK grocery retailer’s customer panel was used for this research, however, some limitations must be noted. For example, the results may be more representative of that specific retailer and cannot be generalised outside the UK context as the data was collected only from participants across the UK. Another limitation is that people on the panel were incentivised with a chance of winning a prize when they participated in the survey which may have influenced their responses and finally, only 19% of the panel participants belong to the millennial generation.



A Appendix: Scenario-based Survey



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Acknowledgements. We would like to acknowledge the support of SAS UK and the contributions of Patricia Barzotti and Charlotte Nielsen from Regent’s University London, MSc Digital Marketing & Analytics.

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